Using classification and regression trees, liquid-based cytology and nuclear morphometry for the discrimination of endometrial lesions

Diagnostic Cytopathology

Volumn 42, Issue 7, 2014, Pages 582-591

  Pouliakis, Abraham ^a   Margari, Charalampia ^b   Margari, Niki ^a   Chrelias, Charalampos ^a   Zygouris, Dimitrios ^a   Meristoudis, Christos ^a   Panayiotides, Ioannis ^a   Karakitsos, Petros ^a  

^a UNIVERSITY OF ATHENS   (Greece)

^b Oncology Department Evaggelismo Hospital   (Greece)

Author keywords

artificial intelligence; classification and regression trees; endometrial cytology; image processing; liquid based cytology

Indexed keywords

ACCURACY; ADULT; ARTICLE; CELL NUCLEUS; CLASSIFICATION; CLASSIFICATION AND REGRESSION TREES; CYTODIAGNOSIS; DISEASE CLASSIFICATION; ENDOMETRIAL DISEASE; ENDOMETRIUM CYTOLOGY; ENDOMETRIUM HYPERPLASIA; FEMALE; HISTOPATHOLOGY; HUMAN; HUMAN TISSUE; IMAGE ANALYSIS; IMAGE PROCESSING; KNOWLEDGE; MAJOR CLINICAL STUDY; MORPHOMETRICS; PERFORMANCE; PRIORITY JOURNAL; SMEAR; TRAINING; DIFFERENTIAL DIAGNOSIS; ENDOMETRIAL NEOPLASMS; ENDOMETRIUM; NUCLEAR SHAPE; PATHOLOGY; REGRESSION ANALYSIS; SENSITIVITY AND SPECIFICITY;

CELL NUCLEUS; CELL NUCLEUS SHAPE; CYTODIAGNOSIS; DIAGNOSIS, DIFFERENTIAL; ENDOMETRIAL NEOPLASMS; ENDOMETRIUM; FEMALE; HUMANS; REGRESSION ANALYSIS; SENSITIVITY AND SPECIFICITY;

EID: 84902539963     PISSN: 87551039     EISSN: 10970339     Source Type: Journal    
DOI: 10.1002/dc.23077     Document Type: Article

References (71)

1. Shyamala G, Ferenczy A,. 1981. The effect of sodium molybdate on the cytoplasmic estrogen and progesterone receptors in human endometrial tissues. Diagn Gynecol Obstet 3: 277-282.
2. Murphy LJ, Murphy LC, Friesen HG,. 1987. Estrogen induces insulin-like growth factor-I expression in the rat uterus. Mol Endocrinol 1: 445-450.
3. Ferenczy A, Bergeron C,. Endometrial hyperplasia. In:, Lowe D, Fox H, editors. Advances in gynaecological pathology. Edinburgh: Churchill Livingstone; 1992. p 207-234.
4. Karakitsos P, Cochand-Priollet B, Guillausseau PJ, Pouliakis A,. 1996. Potential of the back propagation neural network in the morphologic examination of thyroid lesions. Anal Quant Cytol Histol 18: 494-500.
5. Marchevsky AM, Tsou JA, Laird-Offringa IA,. 2004. Classification of individual lung cancer cell lines based on DNA methylation markers: Use of linear discriminant analysis and artificial neural networks. J Mol Diagn 6: 28-36.
6. Markopoulos C, Karakitsos P, Botsoli-Stergiou E, Pouliakis A, Ioakim-Liossi A, Kyrkou K, Gogas J,. 1997. Application of the learning vector quantizer to the classification of breast lesions. Anal Quant Cytol Histol 19: 453-460.
7. Pantazopoulos D, Karakitsos P, Iokim-Liossi A, Pouliakis A, Botsoli-Stergiou E, Dimopoulos C,. 1998. Back propagation neural network in the discrimination of benign from malignant lower urinary tract lesions. J Urol 159: 1619-1623.
8. Astion ML, Wilding P,. 1992. Application of neural networks to the interpretation of laboratory data in cancer diagnosis. Clin Chem 38: 34-38.
9. Cochand-Priollet B, Koutroumbas K, Megalopoulou TM, Pouliakis A, Sivolapenko G, Karakitsos P,. 2006. Discriminating benign from malignant thyroid lesions using artificial intelligence and statistical selection of morphometric features. Oncol Rep 15: 1023-1026.
10. Chien CW, Lee YC, Ma T, Lee TS, Lin YC, Wang W, Lee WJ,. 2008. The application of artificial neural networks and decision tree model in predicting post-operative complication for gastric cancer patients. Hepatogastroenterology 55: 1140-1145.
11. Yamamura Y, Nakajima T, Ohta K, Nashimoto A, Arai K, Hiratsuka M, Sasako M, Kodera Y, Goto M,. 2002. Determining prognostic factors for gastric cancer using the regression tree method. Gastric Cancer 5: 201-207.
12. Karakitsos P, Megalopoulou TM, Pouliakis A, Tzivras M, Archimandritis A, Kyroudes A,. 2004. Application of discriminant analysis and quantitative cytologic examination to gastric lesions. Anal Quant Cytol Histol 26: 314-322.
13. Ljung BM, Chew KL, Moore DH, 2nd, King EB,. 2004. Cytology of ductal lavage fluid of the breast. Diagn Cytopathol 30: 143-150.
14. Wolberg WH, Tanner MA, Loh WY, Vanichsetakul N,. 1987. Statistical approach to fine needle aspiration diagnosis of breast masses. Acta Cytol 31: 737-741.
15. Markopoulos C, Karakitsos P, Botsoli-Stergiou E, Pouliakis A, Gogas J, Ioakim-Liossi A, Kyrkou K,. 1997. Application of back propagation to the diagnosis of breast lesions by fine needle aspiration. Breast 6: 293-298.
16. Dey P, Logasundaram R, Joshi K,. 2013. Artificial neural network in diagnosis of lobular carcinoma of breast in fine-needle aspiration cytology. Diagn Cytopathol 41: 102-106.
17. Schaffer AA, Simon R, Desper R, Richter J, Sauter G,. 2001. Tree models for dependent copy number changes in bladder cancer. Int J Oncol 18: 349-354.
18. Karakitsos P, Pouliakis A, Kordalis G, Georgoulakis J, Kittas C, Kyroudes A,. 2005. Potential of radial basis function neural networks in discriminating benign from malignant lesions of the lower urinary tract. Anal Quant Cytol Histol 27: 35-42.
19. Vriesema JL, van der Poel HG, Debruyne FM, Schalken JA, Kok LP, Boon ME,. 2000. Neural network-based digitized cell image diagnosis of bladder wash cytology. Diagn Cytopathol 23: 171-179.
20. Giovagnoli MR, Cenci M, Olla SV, Vecchione A,. 2002. Cervical false negative cases detected by neural network-based technology. Critical review of cytologic errors. Acta Cytol 46: 1105-1109.
21. Kok MR, van Der Schouw YT, Boon ME, Grobbee DE, Kok LP, Schreiner-Kok PG, van der Graaf Y, Doornewaard H, van den Tweel JG,. 2001. Neural network-based screening (NNS) in cervical cytology: No need for the light microscope? Diagn Cytopathol 24: 426-434.
22. Karakitsos P, Pouliakis A, Meristoudis C, Margari N, Kassanos D, Kyrgiou M, Panayiotides JG, Paraskevaidis E,. 2011. A preliminary study of the potential of tree classifiers in triage of high-grade squamous intraepithelial lesions. Anal Quant Cytol Histol 33: 132-140.
23. Kok MR, Habers MA, Schreiner-Kok PG, Boon ME,. 1998. New paradigm for ASCUS diagnosis using neural networks. Diagn Cytopathol 19: 361-366.
24. Boon ME, Kok LP,. 1993. Neural network processing can provide means to catch errors that slip through human screening of pap smears. Diagn Cytopathol 9: 411-416.
25. Varlatzidou A, Pouliakis A, Stamataki M, Meristoudis C, Margari N, Peros G, Panayiotides JG, Karakitsos P,. 2011. Cascaded learning vector quantizer neural networks for the discrimination of thyroid lesions. Anal Quant Cytol Histol 33: 323-334.
26. Rorive S, N Dh, Fossion C, Delpierre I, Arbaguia N, Avni F, Decaestecker C, Salmon I,. 2010. Ultrasound-guided fine-needle aspiration of thyroid nodules: Stratification of malignancy risk using follicular proliferation grading, clinical and ultrasonographic features. Eur J Endocrinol 162: 1107-1115.
27. Haymart MR, Cayo M, Chen H,. 2009. Papillary thyroid microcarcinomas: Big decisions for a small tumor. Ann Surg Oncol 16: 3132-3139.
28. Karakitsos P, Cochand-Priollet B, Pouliakis A, Guillausseau PJ, Ioakim-Liossi A,. 1999. Learning vector quantizer in the investigation of thyroid lesions. Anal Quant Cytol Histol 21: 201-208.
29. Atkinson EN, Mitchell MF, Ramanujam N, Richards-Kortum R,. 1995. Statistical techniques for diagnosing CIN using fluorescence spectroscopy: SVD and CART. J Cell Biochem Suppl 23: 125-130.
30. Deshpande V, Kapila K, Sai KS, Verma K,. 1997. Follicular neoplasms of the thyroid. Decision tree approach using morphologic and morphometric parameters. Acta Cytol 41: 369-376.
31. Breiman L, Friedman J, Stone CJ, Olshen RA,. Classification and regression trees. Belmont, CA: Wadsworth International Group; 1984. X, 358 p.
32. Ausems EW, van der Kamp JK, Baak JP,. 1985. Nuclear morphometry in the determination of the prognosis of marked atypical endometrial hyperplasia. Int J Gynecol Pathol 4: 180-185.
33. Baak JP, Nauta JJ, Wisse-Brekelmans EC, Bezemer PD,. 1988. Architectural and nuclear morphometrical features together are more important prognosticators in endometrial hyperplasias than nuclear morphometrical features alone. J Pathol 154: 335-341.
34. Colgan TJ, Norris HJ, Foster W, Kurman RJ, Fox CH,. 1983. Predicting the outcome of endometrial hyperplasia by quantitative analysis of nuclear features using a linear discriminant function. Int J Gynecol Pathol 1: 347-352.
35. Fu YS, Ferenczy A, Huang I, Gelfand MM,. 1988. Digital imaging analysis of normal, hyperplastic and malignant endometrial cells in endometrial brushing samples. Anal Quant Cytol Histol 10: 139-149.
36. Norris HJ, Becker RL, Mikel UV,. 1989. A comparative morphometric and cytophotometric study of endometrial hyperplasia, atypical hyperplasia, and endometrial carcinoma. Hum Pathol 20: 219-223.
37. Tezuka F, Chiba R, Takahashi T,. 1994. Morphometric and multivariate statistical detection of cancer cells in endometrial cytology. Anal Quant Cytol Histol 16: 332-338.
38. Tezuka F, Chiba R, Takahashi T,. 1995. Quantitative cytopathology of endometrial lesions. J Cell Biochem Suppl 23: 147-150.
39. Baak JP, Kurver PH, Overdiep SH, Delemarre JF, Boon ME, Lindeman J, Diegenbach PC,. 1981. Quantitative, microscopical, computer-aided diagnosis of endometrial hyperplasia or carcinoma in individual patients. Histopathology 5: 689-695.
40. Karakitsos P, Kyroudes A, Pouliakis A, Stergiou EB, Voulgaris Z, Kittas C,. 2002. Potential of the learning vector quantizer in the cell classification of endometrial lesions in postmenopausal women. Anal Quant Cytol Histol 24: 30-38.
41. Fox H,. 1984. The endometrial hyperplasias. Obstet Gynecol Annu 13: 197-209.
42. Kurman RJ, Ellenson LH, Ronnett BM,. Blaustein's pathology of the female genital tract. New York, NY: Springer; 2011 xv, 1246 p.
43. Baxes GA,. Digital image processing: Principles and applications. New York: Wiley; 1994. xviii, 452 p.
44. Bibbo M, Bartels PH, Galera-Davidson H, Dytch HE, Wied GL,. 1986. Markers for malignancy in the nuclear texture of histologically normal tissue from patients with thyroid tumors. Anal Quant Cytol Histol 8: 168-176.
45. Jain AK,. Fundamentals of digital image processing. Englewood Cliffs, NJ: Prentice Hall; 1989. xxi, 569 p.
46. Pitas I,. Digital image processing algorithms and applications. New York: Wiley; 2000. 419 p.
47. Sonka M, Hlavac V, Boyle R,. Image processing, analysis, and machine vision. London, New York: Chapman & Hall Computing; 1993. xix, 555 p.
48. Sá JPMd. Applied statistics: Using SPSS, Statistica, MATLAB, and R. Berlin; New York: Springer; 2007. xxiv, 505 p.
49. Tezuka F, Namiki T, Higashiiwai H,. 1992. Observer variability in endometrial cytology using kappa statistics. J Clin Pathol 45: 292-294.
50. Reagan JW,. 1980. Can screening for endometrial cancer be justified? Acta Cytol 24: 87-89.
51. Nagai S, Sawada T,. 1983. [Cytological findings on endometrial carcinoma and endometrial hyperplasia]. (abstract) Nihon Sanka Fujinka Gakkai Zasshi 35: 2283-2290.
52. Yanoh K, Norimatsu Y, Hirai Y, Takeshima N, Kamimori A, Nakamura Y, Shimizu K, Kobayashi TK, Murata T, Shiraishi T,. 2009. New diagnostic reporting format for endometrial cytology based on cytoarchitectural criteria. Cytopathology 20: 388-394.
53. Papaefthimiou M, Symiakaki H, Mentzelopoulou P, Tsiveleka A, Kyroudes A, Voulgaris Z, Tzonou A, Karakitsos P,. 2005. Study on the morphology and reproducibility of the diagnosis of endometrial lesions utilizing liquid-based cytology. Cancer 105: 56-64.
54. Papaefthimiou M, Symiakaki H, Mentzelopoulou P, Giahnaki AE, Voulgaris Z, Diakomanolis E, Kyroudes A, Karakitsos P,. 2005. The role of liquid-based cytology associated with curettage in the investigation of endometrial lesions from postmenopausal women. Cytopathology 16: 32-39.
55. Norimatsu Y, Shimizu K, Kobayashi TK, Moriya T, Tsukayama C, Miyake Y, Ohno E,. 2006. Cellular features of endometrial hyperplasia and well differentiated adenocarcinoma using the Endocyte sampler: Diagnostic criteria based on the cytoarchitecture of tissue fragments. Cancer 108: 77-85.
56. Watanabe J, Nishimura Y, Tsunoda S, Kawaguchi M, Okayasu I, Kuramoto H,. 2009. Liquid-based preparation for endometrial cytology-Usefulness for predicting the prognosis of endometrial carcinoma preoperatively. Cancer 117: 254-263.
57. Kyroudi A, Paefthimiou M, Symiakaki H, Mentzelopoulou P, Voulgaris Z, Karakitsos P,. 2006. Increasing diagnostic accuracy with a cell block preparation from thin-layer endometrial cytology: A feasibility study. Acta Cytol 50: 63-69.
58. Norimatsu Y, Kouda H, Kobayashi TK, Moriya T, Yanoh K, Tsukayama C, Miyake Y, Ohno E,. 2008. Utility of thin-layer preparations in the endometrial cytology: Evaluation of benign endometrial lesions. Ann Diagn Pathol 12: 103-111.
59. Norimatsu Y, Kouda H, Kobayashi TK, Shimizu K, Yanoh K, Tsukayama C, Miyake Y, Ohno E,. 2009. Utility of liquid-based cytology in endometrial pathology: Diagnosis of endometrial carcinoma. Cytopathology 20: 395-402.
60. Norimatsu Y, Shigematsu Y, Sakamoto S, Ohsaki H, Yanoh K, Kawanishi N, Kobayashi TK,. 2012. Nuclear features in endometrial cytology: Comparison of endometrial glandular and stromal breakdown and endometrioid adenocarcinoma grade 1. Diagn Cytopathol 12: 1077-1082.
61. Firat P, Mocan G, Kapucuoglu N,. 2002. Liquid-based endometrial cytology: Endometrial sample collection by using Tao brush. Diagn Cytopathol 27: 393-394.
62. Garcia F, Barker B, Davis J, Shelton T, Harrigill K, Schalk N, Meyer J, Hatch K,. 2003. Thin-layer cytology and histopathology in the evaluation of abnormal uterine bleeding. J Reprod Med 48: 882-888.
63. Fambrini M, Buccoliero AM, Bargelli G, Cioni R, Piciocchi L, Pieralli A, Andersson KL, Scarselli G, Taddei G, Marchionni M,. 2008. Clinical utility of liquid-based cytology for the characterization and management of endometrial polyps in postmenopausal age. Int J Gynecol Cancer 18: 306-311.
64. Buccoliero AM, Castiglione F, Gheri CF, Garbini F, Fambrini M, Bargelli G, Pappalardo S, Scarselli G, Marchionni M, Taddei GL,. 2007. Liquid-based endometrial cytology: Its possible value in postmenopausal asymptomatic women. Int J Gynecol Cancer 17: 182-187.
65. Kipp BR, Medeiros F, Campion MB, Distad TJ, Peterson LM, Keeney GL, Halling KC, Clayton AC,. 2008. Direct uterine sampling with the Tao brush sampler using a liquid-based preparation method for the detection of endometrial cancer and atypical hyperplasia: A feasibility study. Cancer 114: 228-235.
66. Sams SB, Currens HS, Raab SS,. 2012. Liquid-based Papanicolaou tests in endometrial carcinoma diagnosis. Performance, error root cause analysis, and quality improvement. Am J Clin Pathol 137: 248-254.
67. Michael CW, McConnel J, Pecott J, Afify AM, Al-Khafaji B,. 2001. Comparison of ThinPrep and TriPath PREP liquid-based preparations in nongynecologic specimens: A pilot study. Diagn Cytopathol 25: 177-184.
68. Rossi ED, Zannoni GF, Lombardi CP, Vellone VG, Moncelsi S, Papi G, Pontecorvi A, Fadda G,. 2012. Morphological and immunocytochemical diagnosis of thyroiditis: Comparison between conventional and liquid-based cytology. Diagn Cytopathol 40: 404-409.
69. Cochand-Priollet B, Cartier I, de Cremoux P, Le Gales C, Ziol M, Molinie V, Petitjean A, Dosda A, Merea E, Biaggi A, Gouget I, Arkwright S, Vacher-Lavenu MC, Vielh P, Coste J,. 2005. Cost-effectiveness of liquid-based cytology with or without hybrid-capture II HPV test compared with conventional Pap smears: A study by the French Society of Clinical Cytology. Diagn Cytopathol 33: 338-343.
70. Selvaggi SM,. 2005. Background features of endometrial carcinoma on ThinPrep cytology. Diagn Cytopathol 33: 162-165.
71. Guidos BJ, Selvaggi SM,. 2000. Detection of endometrial adenocarcinoma with the ThinPrep Pap test. Diagn Cytopathol 23: 260-265.